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This work shows how deep learning techniques can benefit to remote sensing. We focus on tasks which are recurrent in Earth Observation data analysis. For classification and semantic mapping of aerial images, we present various deep network architectures and show that context information and dense labeling allow to reach better performances. For estimation of normals in point clouds, combining Hough transform with convolutional networks also improves the accuracy of previous frameworks bydoi:10.1109/jurse.2017.7924536 dblp:conf/jurse/AudebertBRSFLM17 fatcat:4x55h56kifhmpgqoaqnhxckgu4